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Abstract

Fragments of documents are common subjects in forensic analysis of questioned documents. Forensic analysis of torn document is more challenging owing to sparse data content; for example, a document fragment might consist of only part of a word. The degree of difficulty increases when large number of such documents needs to be analyzed. A forensic expert might overlook evidences in this huge pool of data. This dissertation aims to help combat this problem by studying scientific methodologies that can narrow down the search space of a forensic expert. Automatic sorting of document fragments can be accomplished based on criteria set by the forensic expert. This demands execution of the following :(i) text/graphics segmentation;(ii) segmentation of text type (printed/handwritten);(iii) script identification of text;(iv) identification of the writer; (v) identifying the font of the printed text. Adopting various image processing and pattern recognition techniques certain methodologies are proposed for accomplishing such tasks. Rigorous experiments have been carried out to evaluate our scientific methodologies with real life torn document fragments. Feature encoding techniques have been meticulously chosen so that discriminative properties between different objects of interest are well represented, making the classification task easier. For e.g. in case of writer identification we have implemented a feature encoding scheme that reveals variations in character shape structures between different writers. The thesis consists of 10 chapters.